Peptide liquid enrichment and hydroponic irrigation intelligent fish and vegetable peptide integration method and system

Abstract

The invention provides a peptide liquid enrichment and hydroponic irrigation intelligent fish and vegetable peptide integration method, which comprises the following steps: S1, obtaining a preset production strategy selected by a user through an input device, and obtaining an operation mode selected by the user; s2, according to the operation mode, adopting a multi-target collaborative optimization algorithm to generate operation instructions of a culture unit, a water culture unit and a peptide extraction unit; s3, sending the operation instruction to the culture unit, the water culture unit and the peptide extraction unit through a standardized interface, and obtaining the operation state of each unit; s4, adjusting a water flow parameter of the culture unit according to the running state, and generating a first water body state; s5, extracting bioactive peptides from the first water body state to generate a second water body state; s6, adjusting nutrient solution supply parameters according to the second water body state, and generating a plant growth state; s7, the treated third water body state flows back to the culture unit, culture parameters are adjusted, and the collaborative result of fish growth and water quality optimization is achieved.

Description

Technical Field

[0001] This invention relates to the field of modern agricultural technology, and in particular to an integrated method and system for peptide enrichment and intelligent aquaponics peptide integration in hydroponic irrigation. Background Technology

[0002] In the field of modern agriculture and ecological cycles, researching how to integrate multiple production objectives into a single system is particularly crucial. This area not only concerns food security and resource conservation but also directly impacts the balance between environmental sustainability and economic benefits. Constructing an efficient and synergistic ecological cycle system by integrating fish farming, plant cultivation, and bio-resource extraction has become an important direction for promoting green agriculture.

[0003] However, existing methods often struggle to achieve efficient resource allocation and dynamic balance across various stages when faced with multi-objective production demands. Many solutions lack in-depth consideration of the interrelationships between different production objectives, leading to situations where, with limited resources, prioritizing one objective often comes at the expense of others. For example, pursuing rapid fish growth may neglect the nutritional needs of plants, or the extraction of biological resources may overlook the impact on the water cycle, resulting in inefficient operation of the overall system.

[0004] A deeper technical challenge lies in how to dynamically coordinate the priorities and operating parameters among various production stages under limited resources. This coordination capability directly determines whether the system can flexibly adjust to different needs. For example, in fish farming, the nutrients in the water need to meet the growth requirements of the fish, but these nutrients are also an important source for hydroponic plants. Improper priority allocation may lead to limited fish growth or reduced plant yields. The situation becomes even more complex when a biological resource extraction stage is added, as the extraction process may further deplete key components in the water, affecting the normal operation of other stages.

[0005] Therefore, in a resource-limited environment, how to dynamically adjust the operating parameters among fish farming, hydroponics, and biological resource extraction according to the priority of different production goals, and ensure that the nutrient cycle and resource utilization among the three reach the best balance, has become a key issue in promoting the development of this field. Summary of the Invention

[0006] To address the problems mentioned above in the background art, a first aspect of the present invention provides an integrated method for peptide liquid enrichment and intelligent aquaponics peptide cultivation and irrigation, comprising: S1, obtain the preset production strategy selected by the user through the input device, and obtain the operating mode selected by the user; S2, Based on the operating mode, a multi-objective collaborative optimization algorithm is used to generate operating instructions for the aquaculture unit, hydroponic unit, and peptide extraction unit; S3, the operation instructions are sent to the breeding unit, the hydroponic unit and the peptide extraction unit through a standardized interface to obtain the operation status of each unit; S4, adjust the water flow parameters of the aquaculture unit according to the operating status to generate the first water body state; S5, extract bioactive peptides from the first water body state to generate the second water body state; S6, adjust the nutrient solution supply parameters according to the state of the second water body to generate the plant growth state; S7, the treated third water body is returned to the aquaculture unit, the aquaculture parameters are adjusted, and the synergistic result of fish growth and water quality optimization is achieved.

[0007] Optionally, step S1 further includes: Step S11: Obtain the preset production strategy input by the user through the touch screen. The preset production strategy includes peptide priority mode, fish weight gain priority mode and vegetable high yield mode. Step S12: Determine the operating mode based on user input and generate priority weights corresponding to the operating mode; Step S13: The priority weights are transmitted to the central control unit to generate operating instructions for each unit.

[0008] Optionally, step S12 further includes: in peptide priority mode, the weight of bioactive peptide extraction is set to 60% to 70% of the total target weight, the weight of fish growth is set to 20% to 25% of the total target weight, and the weight of plant cultivation is set to 10% to 15% of the total target weight.

[0009] Optionally, step S12 further includes: in the fish weight gain priority mode, the fish growth weight is set to 50% to 60% of the total target weight, the bioactive peptide extraction weight is set to 25% to 30% of the total target weight, and the plant cultivation weight is set to 15% to 20% of the total target weight.

[0010] Optionally, step S12 further includes: in the high-yield vegetable mode, the plant cultivation weight is set to 45% to 55% of the total target weight, the fish growth weight is set to 30% to 35% of the total target weight, and the bioactive peptide extraction weight is set to 15% to 20% of the total target weight.

[0011] Optionally, step S2 further includes: Step S21: Determine the weights of the operating parameters of the aquaculture unit, the hydroponic unit, and the peptide extraction unit according to the operating mode; Step S22: Dynamically adjust the weights of the operating parameters using the multi-objective collaborative optimization algorithm to generate collaborative operating instructions for each unit; Step S23: Determine the operating frequency and parameter range of each unit according to the cooperative operation instructions.

[0012] Optionally, step S3 further includes: Step S31: Transmit the operating instructions to the aquaculture unit, the hydroponic unit, and the peptide extraction unit via a standardized circuit connector; Step S32: Obtain the operation feedback data of each unit and determine the collaborative operation status of each unit; Step S33: Update the control parameters of the central control unit according to the cooperative operation status.

[0013] Optionally, step S4 further includes: Step S41: If the operating mode is peptide-priority mode, then execute the high-frequency stress induction program; Step S42: Adjust the water flow speed through an intermittent water flow speed change program to induce a stress response in the fish; Step S43: Monitor the state of the first water body based on water quality data and determine the stress-inducing factors in the water body.

[0014] Optionally, step S42 further includes: the water flow speed change program adopts a three-stage adjustment mode, the first stage is a slow acceleration stage, in which the water flow speed gradually increases from a stationary state to the target speed, and the acceleration time is 3 to 5 minutes; the second stage is a constant speed maintenance stage, in which the water flow maintains the target speed for 10 to 15 minutes; and the third stage is a slow deceleration stage, in which the water flow speed gradually decreases to a stationary state, and the deceleration time is 2 to 3 minutes.

[0015] A second aspect of the present invention provides an integrated intelligent aquaponics peptide system for peptide enrichment and hydroponic irrigation, which performs integrated intelligent aquaponics peptide enrichment and hydroponic irrigation using the method described above. The system includes: The input module is used to obtain the preset production strategy selected by the user through the input device and to obtain the operating mode selected by the user. The generation module is used to generate operating instructions for the aquaculture unit, hydroponic unit, and peptide extraction unit according to the operating mode using a multi-objective collaborative optimization algorithm. The sending module is used to send the running instructions to the breeding unit, the hydroponic unit and the peptide extraction unit through a standardized interface, and to obtain the running status of each unit; An adjustment module is used to adjust the water flow parameters of the aquaculture unit according to the operating status to generate a first water body state. The extraction module is used to extract bioactive peptides from the first water body state to generate a second water body state; The supply module is used to adjust the nutrient solution supply parameters according to the state of the second water body to generate the plant growth status. The collaborative module is used to return the treated third water body state to the aquaculture unit, adjust the aquaculture parameters, and achieve a synergistic result of fish growth and water quality optimization.

[0016] The technical solution provided by this invention has the following beneficial effects: This invention discloses an integrated method and system for peptide liquid enrichment and hydroponic irrigation of aquaponics peptides. By constructing a synergistic ecological cycle of fish farming, hydroponics, and bioactive peptide extraction, it solves a unique business scenario problem: how to balance multi-objective production needs under limited resource conditions and maximize fish growth, high plant yield, and peptide extraction efficiency.

[0017] This invention utilizes a multi-objective collaborative optimization algorithm to dynamically adjust the operating parameters and priority weights of each unit. Based on the user-selected peptide-first, fish weight-gain-first, or vegetable high-yield modes, it generates precise operating instructions to ensure efficient resource allocation. Simultaneously, this invention employs standardized interfaces and a central control unit to achieve seamless collaboration among units, combined with real-time monitoring and dynamic feedback mechanisms to optimize water circulation and nutrient utilization.

[0018] Ultimately, this invention significantly improves resource utilization, reduces environmental pollution, achieves a balance between economic and ecological benefits, and demonstrates the innovative value of intelligent and circular production. Attached Figure Description

[0019] Figure 1 This is a flowchart of an intelligent aquaponics peptide integration method for peptide enrichment and hydroponic irrigation according to the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of an intelligent aquaponics peptide integrated system for peptide enrichment and hydroponic irrigation according to the present invention. Detailed Implementation

[0021] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0022] like Figure 1As shown, the first aspect of the present invention provides an integrated method for intelligent aquaponics peptide enrichment and hydroponic irrigation, which realizes integrated production of fish farming, hydroponics and bioactive peptide extraction through multi-unit collaborative operation, forming a circular ecosystem.

[0023] S1, obtain the preset production strategy selected by the user through the input device, and obtain the operating mode selected by the user.

[0024] In one embodiment, the preset production strategy includes three basic operating modes: peptide-priority mode, fish weight gain-priority mode, and high-yield vegetable mode. The peptide-priority mode primarily targets the efficient extraction of bioactive peptides by adjusting aquaculture environmental parameters to induce greater stress responses in fish, thereby increasing the content of bioactive peptides in the water. The fish weight gain-priority mode focuses on rapid fish growth, maximizing fish weight gain through optimized feeding strategies, water quality management, and environmental control. The high-yield vegetable mode focuses on high-yield plant cultivation, improving plant yield and quality through precise control of nutrient solution ratios, light conditions, and the growth environment.

[0025] Optionally, this step also includes: Step S11: Obtain the preset production strategy input by the user through the touch screen. The preset production strategy includes peptide priority mode, fish weight gain priority mode and vegetable high yield mode.

[0026] Specifically, the touchscreen serves as the human-machine interface, providing an intuitive interface for users to select different production strategies. The touchscreen interface design includes three main option areas, each corresponding to a production mode and displaying the expected output indicators and operating characteristics of that mode. Users select a mode by clicking the corresponding area, and the system automatically records the user's selection and generates the corresponding operating parameters. The touchscreen also features parameter adjustment capabilities, allowing users to fine-tune specific parameters based on the selected basic mode, such as adjusting the target concentration of peptide extraction, the expected weight gain rate of fish, or the yield target of vegetables.

[0027] In one possible implementation, the peptide-first mode interface displays key information including expected peptide yield, extraction cycle, and impact on fish growth. The fish weight gain-first mode interface displays parameters such as expected weight gain rate, feed conversion ratio, and culture cycle. The high-yield vegetable mode interface displays indicators such as expected yield, growth cycle, and nutrient solution consumption. Users select the most suitable operating mode based on actual production needs and market conditions.

[0028] Step S12: Determine the operating mode based on user input and generate priority weights corresponding to the operating mode.

[0029] In one embodiment, for the sake of brevity, the operating mode corresponds to the production strategy. For example, if the production strategy includes a peptide-priority mode, a fish weight gain-priority mode, and a vegetable high-yield mode, then the operating mode also corresponds to the peptide-priority mode, the fish weight gain-priority mode, and the vegetable high-yield mode, respectively.

[0030] Priority weights are core parameters in multi-objective collaborative optimization algorithms, used to balance the importance of different production objectives. In peptide-first mode, the weight of bioactive peptide extraction is set to the highest, typically accounting for 60% to 70% of the total objective weight; fish growth accounts for approximately 20% to 25% of the total objective weight; and plant cultivation accounts for approximately 10% to 15% of the total objective weight. This weight allocation ensures that the system prioritizes peptide extraction while also considering other production objectives.

[0031] For example, in the fish weight gain priority mode, the weight of fish growth is increased to 50% to 60% of the total target weight, the weight of bioactive peptide extraction is reduced to 25% to 30% of the total target weight, and the weight of plant cultivation is maintained at 15% to 20% of the total target weight. This adjustment makes the system focus more on optimizing the fish's growth environment and reduces stress-induced operations that may affect fish health. In the vegetable high-yield mode, the weight of plant cultivation is increased to 45% to 55% of the total target weight, the weight of fish growth is approximately 30% to 35% of the total target weight, and the weight of bioactive peptide extraction is reduced to 15% to 20% of the total target weight.

[0032] The weighting process also considers dynamic factors such as seasonality, market demand, and equipment status. For example, during peak vegetable demand seasons, the system can automatically adjust weight allocation, appropriately increasing the priority of plant cultivation. During equipment maintenance, the system reduces the operational intensity of relevant units and adjusts weight allocation to ensure overall production stability.

[0033] Step S13: The priority weights are transmitted to the central control unit to generate operating instructions for each unit. In one embodiment, after receiving the priority weights, the central control unit generates specific operating instructions through a built-in decision algorithm. This decision algorithm comprehensively considers multiple factors such as the current operating status of each unit, historical production data, environmental parameters, and equipment performance. The operating instructions include detailed information such as the start and stop times, operating intensity, parameter settings, and coordination methods for each unit.

[0034] In one embodiment, the central control unit employs a hierarchical decision-making structure. First, it determines the overall operational strategy based on priority weights, and then decomposes it into specific tasks for each unit. The operational instructions for the aquaculture unit include water flow control parameters, feeding time and frequency, and water quality monitoring intervals. The operational instructions for the hydroponics unit cover nutrient solution supply time, concentration adjustment, light control, and environmental parameter settings. The operational instructions for the peptide extraction unit include extraction frequency, filtration parameters, membrane operation time, and cleaning cycle.

[0035] It should be noted that the generation of operating instructions also takes into account the synergistic relationships between the various units. For example, when the peptide extraction unit requires high-intensity operation, the aquaculture unit will adjust the frequency and intensity of the stress induction program accordingly, while the hydroponics unit will prepare to receive the treated water and adjust the nutrient solution ratio. This synergistic mechanism ensures that the units can cooperate in an orderly manner, avoiding resource conflicts and efficiency losses.

[0036] S2, Based on the operating mode, a multi-objective collaborative optimization algorithm is used to generate operating instructions for the aquaculture unit, hydroponic unit, and peptide extraction unit.

[0037] In one embodiment, a multi-objective collaborative optimization algorithm is one of the core technologies of this invention. This algorithm can find the optimal balance point among multiple mutually constraining objectives. The basic principle of the algorithm is to transform each production objective into a quantifiable objective function through mathematical modeling, and then use optimization theory to find the parameter combination that maximizes the overall effect of all objective functions.

[0038] Optionally, this step also includes: Step S21: Determine the weights of the operating parameters of the aquaculture unit, the hydroponic unit, and the peptide extraction unit according to the operating mode.

[0039] In one embodiment, the determination of operational parameter weights involves consideration of multiple dimensions. First, there's the production target dimension; different operating modes correspond to different primary production targets, and weight allocation needs to reflect this priority difference. Second, there's the resource constraint dimension, including factors such as energy consumption limits, equipment capacity, and raw material supply; these constraints affect the actual weight allocation. Third, there's the time dimension; the weight allocation for different production stages may need to be dynamically adjusted to adapt to changes in the production cycle. Specifically, the operational parameter weights for the aquaculture unit include water quality management weight, feeding control weight, stress induction weight, and environmental regulation weight. In the peptide-priority mode, the stress induction weight is significantly increased, reaching 40% to 50% of the total operational parameter weight, to ensure a sufficient stress response to produce more bioactive peptides. Specifically, the water quality management weight is approximately 25% to 30% of the total operational parameter weight, the feeding control weight is approximately 15% to 20%, and the environmental regulation weight is approximately 10% to 15%. The operational parameter weights for the hydroponic unit encompass nutrient solution management weight, light control weight, environmental regulation weight, and growth monitoring weight. In the high-yield vegetable mode, the weight of nutrient solution management is increased to 45% to 55% of the total weight of operating parameters, of which light control accounts for approximately 25% to 30%, environmental regulation accounts for approximately 15% to 20%, and growth monitoring accounts for approximately 5% to 10%. This weight allocation ensures that plants obtain optimal growth conditions. The operating parameter weights of the peptide extraction unit include extraction efficiency weight, separation accuracy weight, equipment maintenance weight, and energy consumption control weight. In the peptide-first mode, the extraction efficiency weight reaches 50% to 60% of the total weight of operating parameters, of which separation accuracy weight is approximately 25% to 30%, equipment maintenance weight is approximately 10% to 15%, and energy consumption control weight is approximately 5% to 10%. Step S22: The operating parameter weights are dynamically adjusted through the multi-objective collaborative optimization algorithm to generate collaborative operation instructions for each unit. In one embodiment, the dynamic adjustment mechanism is an important feature of the multi-objective collaborative optimization algorithm, which can automatically adjust parameter weights according to real-time production conditions and environmental changes. The adjustment process is based on feedback control principles. It continuously monitors the operational performance and output indicators of each unit, assesses the rationality of the current weight allocation, and makes adjustments as necessary. In one possible implementation, the dynamic adjustment algorithm employs a sliding window mechanism, collecting operational data from each unit at fixed time intervals and calculating the deviation between actual output and expected targets. When the deviation exceeds a preset threshold, the algorithm initiates a weight adjustment procedure. The adjustment magnitude is determined based on the magnitude of the deviation and historical adjustment effects to avoid over-adjustment that could lead to system instability. For example, when insufficient stress response in fish is detected, resulting in lower-than-expected bioactive peptide production, the algorithm appropriately increases the weight of stress-induced factors in the aquaculture unit while correspondingly decreasing the weights of other parameters.If plant growth exceeds expectations and nutrient solution consumption is too rapid, the algorithm adjusts the nutrient solution management weight of the hydroponic unit, optimizing the supply strategy to improve utilization efficiency. The generation of collaborative operation instructions considers the mutual influence and dependencies between units. Wastewater generated by the aquaculture unit is the nutrient source for the hydroponic unit, and the treated water from the hydroponic unit flows back to the aquaculture unit, forming a closed-loop cycle. The operation of the peptide extraction unit affects the water composition, thereby affecting fish growth and plant cultivation. Collaborative operation instructions ensure that these interactions produce positive effects and avoid negative impacts. Step S23: Determine the operating frequency and parameter range of each unit according to the collaborative operation instructions. In one embodiment, determining the operating frequency requires balancing production efficiency and equipment lifespan. High-frequency operation can increase output but increases equipment wear and energy consumption. Low-frequency operation, while energy-saving, may not meet production demands. Collaborative operation instructions use optimization algorithms to find the most suitable operating frequency, achieving the best balance between efficiency and cost. Specifically, the operating frequency of the aquaculture unit includes water circulation frequency, feeding frequency, water quality monitoring frequency, and stress induction frequency. In peptide-priority mode, the stress induction frequency may be set to 2 to 3 times per hour, each lasting 10 to 15 minutes. The water circulation frequency is maintained at 4 to 6 times per hour to ensure sufficient water flow. Feeding frequency is adjusted according to the fish's growth stage, typically 3 to 5 times per day. The operating frequencies of the hydroponic unit encompass nutrient solution supply frequency, light cycle frequency, environmental regulation frequency, and growth monitoring frequency. The nutrient solution supply frequency is typically set to 1 to 2 times per hour, adjusted according to plant needs and growth stage. The light cycle frequency is set according to the plant's photoperiodic requirements, generally a 12 to 16-hour light period and an 8 to 12-hour dark period. The operating frequencies of the peptide extraction unit include extraction operation frequency, separation membrane cleaning frequency, equipment maintenance frequency, and quality testing frequency. The extraction operation frequency is determined based on the accumulation rate of bioactive peptides in the water, typically performed every 2 to 4 hours. The separation membrane cleaning frequency is set to 1 to 2 times per day to ensure separation efficiency and equipment performance. Parameter ranges are set based on multiple factors, including equipment performance limitations, production safety requirements, and product quality standards. Each parameter has clearly defined upper and lower limits to ensure the safety and controllability of the operation. The parameter range also has a certain degree of adjustability, allowing for temporary adjustments under special circumstances. S3, the operation instructions are sent to the aquaculture unit, the hydroponic unit, and the peptide extraction unit through a standardized interface to obtain the operating status of each unit. In one embodiment, the standardized interface is a key technology to ensure that each unit can accurately receive and execute operation instructions. This interface uses a unified communication protocol and data format, eliminating compatibility issues between different devices and improving the system's reliability and scalability.

[0040] Optionally, this step further includes: Step S31, transmitting the operating instructions to the aquaculture unit, the hydroponic unit, and the peptide extraction unit via standardized circuit connectors. In one embodiment, the standardized circuit connectors adopt an industrial-grade design, possessing good anti-interference capabilities and environmental adaptability. The connectors support the transmission of multiple signal types, including digital control signals, analog measurement signals, and power supply signals. Each connector has a unique identification code to ensure that instructions can be accurately transmitted to the target unit. In one embodiment, the circuit connectors adopt a hierarchical transmission architecture, allocating different types of signals to different transmission channels. Control instructions are transmitted through high-priority channels to ensure real-time performance and accuracy. Status feedback signals are transmitted through medium-priority channels to ensure timely updates. Logs and diagnostic information are transmitted through low-priority channels to avoid affecting the transmission of critical signals. The connectors also have fault detection and automatic recovery functions. When a transmission anomaly is detected, the system automatically switches to a backup channel to ensure communication continuity. Simultaneously, the system records fault information for subsequent maintenance and optimization. Step S32, acquiring the operating feedback data of each unit to determine the collaborative operating status of each unit. In one embodiment, the operating feedback data is an important basis for evaluating the operating effect and collaborative status of each unit. Feedback data includes multiple aspects such as equipment operating parameters, production indicators, environmental parameters, and abnormal information. Data collection employs a combination of real-time monitoring and periodic summarization to ensure data completeness and timeliness. Specifically, feedback data from the aquaculture unit includes water quality parameters, fish behavior, feeding effectiveness, and equipment operating status. Water quality parameters cover key indicators such as dissolved oxygen, pH, ammonia nitrogen, and nitrite content. Fish behavior is obtained through video monitoring and sensor monitoring, including swimming activity, feeding behavior, and stress response levels. Feeding effectiveness is assessed through feed consumption and fish growth rate. Feedback data from the hydroponic unit includes plant growth parameters, nutrient solution status, environmental conditions, and equipment performance. Plant growth parameters include plant height, leaf number, root development, and biomass accumulation. Nutrient solution status includes concentration, pH, conductivity, and nutrient element content. Environmental conditions include temperature, humidity, light intensity, and air circulation. Feedback data from the peptide extraction unit includes extraction efficiency, product quality, equipment status, and energy consumption. Extraction efficiency is assessed by the amount of bioactive peptides extracted per unit time. Product quality includes indicators such as peptide purity, activity, and stability. Equipment status encompasses separation membrane performance, pump operating status, and filtration system effectiveness. The determination of the coordinated operating status is based on a comprehensive analysis of feedback data from each unit. The system uses a data fusion algorithm to integrate and correlate data from different units, identifying the mutual influences and synergistic effects between them. When the operating indicators of each unit reach the expected range and a virtuous cycle is formed between them, the system determines it to be a good coordinated operating status. Step S33: Update the control parameters of the central control unit according to the coordinated operating status.In one embodiment, updating control parameters is a crucial mechanism for system self-optimization and continuous improvement. The update process is based on machine learning principles, analyzing historical operating data and current state information to identify the optimal parameter combination and applying it to subsequent operational control. In one possible implementation, parameter updates employ incremental learning to avoid completely overwriting historical experience. New operating data is compared and analyzed with historical data to extract valuable improvement information, which is then incorporated into existing control parameters with appropriate weights. This approach adapts to new operating conditions while maintaining system stability. S4, the water flow parameters of the aquaculture unit are adjusted according to the operating state to generate a first water body state. In one embodiment, adjusting water flow parameters is a key factor influencing the physiological state of fish and the production of bioactive peptides. By precisely controlling the water flow velocity, direction, and flow rate, the stress response of fish can be effectively regulated, thereby affecting the secretion of bioactive substances in their bodies. Adjusting water flow parameters requires comprehensive consideration of multiple factors, including fish species, growth stage, environmental conditions, and production goals.

[0041] Optionally, this step further includes: step S41, if the operating mode is peptide-priority mode, then a high-frequency stress induction program is executed. In one embodiment, the high-frequency stress induction program is the core technology of the peptide-priority mode, which induces a moderate stress response in fish through controlled environmental stimulation. The stress response prompts the fish to secrete more bioactive peptides, which enter the aquatic environment through the fish's metabolism. The program design needs to find a balance between increasing peptide production and maintaining fish health. Specifically, the high-frequency stress induction program includes various methods such as temperature fluctuation induction, light change induction, water flow disturbance induction, and sound stimulation induction. Temperature fluctuation induction adjusts the water temperature within a safe range to make the fish feel the environmental changes. The temperature change range is usually controlled within 2 to 3 degrees Celsius, with a frequency of 1 to 2 times per hour, each lasting 15 to 20 minutes. This temperature change simulates the diurnal temperature range in the natural environment and will not harm the fish. Light change induction is achieved by adjusting the light intensity and spectral composition. Light intensity is increased or decreased by 20% to 30% from normal lighting levels, with a cycle of 30 to 45 minutes. Spectral modulation primarily targets the blue and red light components, influencing the fish's circadian rhythms by altering the spectral proportions. Sound stimulation inducement uses low-frequency sound waves, ranging from 20 to 100 Hz, with the volume controlled within a safe range to avoid affecting the fish's auditory system. In one embodiment, the stress induction program employs a combined stimulation approach, combining multiple induction methods according to a preset time sequence. For example, a 5-minute temperature increase, followed by a 10-minute increase in light intensity, and finally a 5-minute gentle water disturbance. This combination produces a more significant stress effect while avoiding the potential for reduced adaptation that might occur with a single stimulation method. It should be noted that the stress induction program also includes a recovery period setting, providing sufficient recovery time after each induction to allow the fish's physiological state to return to normal. The recovery period is typically 2 to 3 times the length of the induction period to ensure that the fish's health is not affected by continuous stress. The program also includes a safety monitoring mechanism that monitors the fish's behavior and physiological indicators in real time, immediately stopping the induction program upon detecting any abnormalities. Step S42 involves adjusting the water flow speed through an intermittent flow rate variation program to induce a stress response in the fish. In one embodiment, the intermittent flow rate variation program is a precise hydrodynamic control technique that creates a dynamic aquatic environment by periodically changing the water flow speed. This program is based on the fish's sensitivity to changes in water flow, using variations in water flow speed to stimulate the fish's sensory and motor systems, thereby triggering a physiological stress response. The core of the program is a water flow speed control algorithm, which designs the variation mode based on the swimming ability and behavioral characteristics of the fish. For fish with strong swimming abilities, such as carp and grass carp, the water flow speed can vary between 10 cm and 30 cm per second. For fish with relatively weak swimming abilities, such as crucian carp and bream, the water flow speed is controlled between 5 cm and 20 cm per second.The speed change is gradually adjusted to avoid excessive shock to the fish from sudden speed changes. In one possible implementation, the water flow speed change program uses a three-stage adjustment mode. The first stage is a slow acceleration phase, where the water flow speed gradually increases from a standstill to the target speed, with an acceleration time of 3 to 5 minutes. The second stage is a constant speed maintenance phase, where the water flow maintains the target speed for 10 to 15 minutes, allowing the fish to adapt and develop corresponding swimming behaviors. The third stage is a slow deceleration phase, where the water flow speed gradually decreases to a standstill, with a deceleration time of 2 to 3 minutes. Adjustment of the water flow direction is also an important part of the program. By changing the direction of the water flow, the fish can be forced to adjust their swimming direction and posture, increasing the intensity of movement and stress. The water flow direction adjustment uses a multi-point water inlet and outlet design, achieving flexible adjustment of the water flow direction by controlling the opening and closing of different inlets and outlets. Commonly used flow directions include clockwise circulation, counterclockwise circulation, and straight convection patterns. The program also considers the impact of water flow turbulence characteristics on fish behavior. Moderate turbulence can increase the oxygen content of water, improve water quality, and also provide additional stimulation to fish. Turbulence intensity is achieved by adjusting the operating parameters of the water pump and the pipeline design, with the turbulence coefficient controlled between 0.1 and 0.3, providing stimulation without harming the fish. Step S43: Monitor the state of the first water body based on water quality data to determine stress-inducing factors in the water. In one embodiment, water quality data monitoring is an important means of assessing stress-inducing effects and water conditions. The monitoring system uses multi-parameter online detection technology to track changes in various chemical and biological indicators in the water in real time. These changes reflect the physiological state and stress response level of fish, providing a scientific basis for subsequent peptide extraction and water treatment. Stress-inducing factors refer to various bioactive substances released into the water by fish during stress. These factors include stress hormones, immune factors, metabolites, and signaling molecules. Stress hormones mainly include cortisol, adrenaline, and noradrenaline, which increase in secretion under stress, and some enter the water through the gills and skin. Immune factors, including various cytokines, antibodies, and complement components, reflect the activation status of the fish's immune system. In one embodiment, the water quality monitoring system comprises three subsystems: physical parameter monitoring, chemical parameter monitoring, and biological parameter monitoring. Physical parameter monitoring covers basic indicators such as water temperature, dissolved oxygen, pH, conductivity, and turbidity. Changes in these parameters reflect the basic state of the water body and the quality of the fish's living environment. Chemical parameter monitoring includes indicators such as ammonia nitrogen, nitrite, nitrate, phosphate, and organic matter content, which reflect the nutrient status and pollution level of the water body. Biological parameter monitoring is the core component of stress-induced factor detection, achieved using biosensor technology and immunoassay technology. Biosensors can specifically identify and quantify specific biomolecules, such as stress hormones and cytokines. Immunoassay technology detects protein-based stress factors in the water body through antigen-antibody reactions.The detection frequency is determined based on the operating cycle of the stress induction program, typically a comprehensive detection every 15 to 30 minutes. The analysis of monitoring data employs multivariate statistical methods and machine learning algorithms to identify the correlations and trends between different parameters. By establishing a mathematical model between the concentration of stress-inducing factors and the parameters of the stress program, the effect of stress induction can be predicted and optimized. The model can also identify abnormal situations, such as over-stress or under-stress, and adjust the parameters of the induction program in a timely manner. The determination of the first water body state is based on a comprehensive evaluation of all monitoring parameters. The system calculates a water body state index using a weighted average algorithm, which comprehensively reflects the water quality and the stress induction effect. When the water body state index reaches the preset target range, it indicates that the stress induction has achieved the expected effect, and sufficient bioactive peptide precursor substances have accumulated in the water body, allowing for the next step of peptide extraction. S5, bioactive peptides are extracted from the first water body state to generate the second water body state. In one embodiment, bioactive peptide extraction is one of the core links of the entire synergistic system, and this process requires efficient separation and purification while maintaining peptide activity. The extraction process employs a combination of physical separation and biochemical treatment to ensure that the extracted bioactive peptides possess good bioactivity and stability.

[0042] Optionally, this step further includes: step S51, extracting bioactive peptides from the first water state using a circulating pump and a separation membrane. In one embodiment, the circulating pump system is the core power source for bioactive peptide extraction, responsible for driving the circulation of water within the extraction system. The circulating pump employs variable frequency control technology, enabling adjustment of flow rate and pressure according to extraction requirements. The pump design takes into account the sensitivity of bioactive peptides, employing a low-shear impeller design to avoid damage to the peptide molecular structure caused by high-speed flow. The pump body material is selected from biocompatible stainless steel or food-grade plastic to prevent the material from affecting peptide activity. Separation membrane technology is a key technology for bioactive peptide extraction, employing a multi-stage membrane separation system to achieve graded extraction of peptides with different molecular weights. The first stage uses a microfiltration membrane with a pore size of 0.1 to 0.5 micrometers, primarily removing suspended particles, bacteria, and large molecular impurities from the water. The second stage uses an ultrafiltration membrane with a molecular weight cutoff range of 1000 to 10000 Daltons, used for concentration and preliminary purification of bioactive peptides. The third stage employs a nanofiltration membrane with a molecular weight cutoff of 200 to 1000 Daltons to achieve precise separation of small peptide molecules. In one possible implementation, the separation membrane system uses a cross-flow filtration mode, where water flows tangentially along the membrane surface, reducing membrane fouling and concentration polarization. The membrane module uses a hollow fiber or flat-plate design, providing a large membrane area and good mass transfer. The membrane material is selected from polyethersulfone, polyvinylidene fluoride, or ceramic materials, exhibiting good chemical stability and mechanical strength. The extraction process also includes pretreatment and post-treatment stages. Pretreatment mainly adjusts the pH and ionic strength of the water to create suitable conditions for membrane separation. The pH is typically adjusted to between 6.5 and 7.5, and the ionic strength is controlled within the range of 0.1 to 0.3 mol / L. Post-treatment includes further purification and activity protection of the concentrate, using freeze-drying or spray-drying techniques to prepare peptide powder products. The coordinated operation of the circulation pump and separation membrane is achieved through an automatic control system. The control system adjusts the pump's operating parameters based on changes in pressure difference, flow rate, and concentration across the membrane to maintain the system in optimal operating condition. When membrane fouling leads to a decrease in flux, the system automatically initiates a backwashing procedure to restore the membrane's separation performance. Step S52 involves dynamically adjusting filtration parameters and operating frequency to generate a second water state. In one embodiment, the dynamic adjustment mechanism is an important means of improving extraction efficiency and product quality. This mechanism, based on real-time monitoring data, automatically adjusts the operating parameters of the filtration system to adapt to changes in water composition and environmental conditions. Adjustment parameters include key factors such as transmembrane pressure difference, flow rate, temperature, and pH. Transmembrane pressure difference is a major parameter affecting membrane separation performance; too low a pressure difference leads to insufficient flux, while too high a pressure difference may damage the membrane structure or affect peptide activity. The dynamic adjustment system monitors the pressure difference across the membrane and automatically adjusts the pump's operating speed and valve opening to maintain the transmembrane pressure difference within the optimal range. For microfiltration membranes, the transmembrane pressure difference is controlled between 0.05 and 0.2 MPa. For ultrafiltration membranes, the transmembrane pressure difference is controlled between 0.1 and 0.5 MPa.For nanofiltration membranes, the transmembrane pressure differential is controlled between 0.5 and 2.0 MPa. Flow rate adjustment mainly targets the tangential flow rate in cross-flow filtration, as this parameter affects the shear force on the membrane surface and mass transfer efficiency. Appropriate tangential flow rates can reduce membrane fouling and improve separation efficiency. Flow rate adjustment is determined based on water viscosity, membrane type, and target product characteristics. Generally, the tangential flow rate is controlled at 1 to 3 meters per second for microfiltration membranes, 2 to 5 meters per second for ultrafiltration membranes, and 3 to 8 meters per second for nanofiltration membranes. Temperature adjustment considers the thermal stability of bioactive peptides and the performance characteristics of the membrane material. Most bioactive peptides have better stability at low temperatures; therefore, the extraction process is usually carried out at low temperatures of 4 to 15 degrees Celsius. The temperature control system uses heat exchangers and refrigeration equipment to precisely control the system temperature. Temperature fluctuations are controlled within ±1 degree Celsius to ensure the stability of the extraction process. Operating frequency adjustment includes both continuous and intermittent operation modes. Continuous operation mode is suitable for large-scale production, with the system running 24 hours a day, maintaining membrane performance through rotational cleaning. Intermittent operation mode is suitable for small-batch or high-quality production. The system operates according to a preset time cycle, each cycle including three stages: extraction, cleaning, and maintenance. In one embodiment, the typical cycle of intermittent operation mode is 4 to 6 hours, with the extraction stage accounting for 70% to 80% of the time, the cleaning stage accounting for 15% to 20% of the time, and the maintenance stage accounting for 5% to 10% of the time. The selection of the operating frequency is determined comprehensively based on the concentration of bioactive peptides in the water, the target yield, and the equipment condition. The second water state refers to the water state after bioactive peptide extraction. Compared with the first water state, the bioactive peptide content in the second water state is significantly reduced, but most nutrients and beneficial microorganisms are still retained. The physicochemical properties of the water also change accordingly, such as a decrease in protein content, a possible slight increase in ammonia nitrogen content, and a more stable pH value. Step S53: Based on the water quality data of the second water state, determine the extraction efficiency and changes in water composition. In one embodiment, the evaluation of extraction efficiency is an important step in optimizing the extraction process and ensuring product quality. Evaluation indicators include multiple aspects such as peptide extraction rate, peptide purity, peptide activity retention rate, and water treatment effect. Peptide extraction rate refers to the ratio of the amount of bioactive peptides extracted from the water to the total peptide content in the water, typically requiring a purity of 80% or higher. Peptide purity refers to the proportion of the target peptide content in the extracted product to the total protein content; high-quality products should have a purity of 90% or higher. Peptide activity retention rate is assessed through bioactivity detection methods, including functional indicators such as antioxidant activity, antibacterial activity, and immunomodulatory activity. Activity detection employs in vitro cell experiments and biochemical analysis methods to assess the impact of the extraction process on peptide bioactivity. Ideally, the activity retention rate should reach 85% or higher to ensure that the extracted bioactive peptides have good application value. Analysis of changes in water composition uses comprehensive water quality testing methods, including conventional physicochemical indicators and specific biological indicators.Conventional indicators include total nitrogen, total phosphorus, chemical oxygen demand (COD), and biochemical oxygen demand (BOD), reflecting changes in the nutrient status and organic matter content of the water body. Specialized indicators include amino acid composition, trace element content, and vitamin content, reflecting changes in the nutritional value of the water body. In one possible implementation, water composition analysis employs advanced analytical methods such as high-performance liquid chromatography (HPLC), mass spectrometry (MS / MS), and atomic absorption spectrometry (AAS). These methods can accurately and quantitatively detect changes in the content of various components in the water body, providing precise data support for subsequent hydroponic nutrient solution preparation. The analytical results are also used to optimize extraction process parameters, improving extraction efficiency and product quality. Dynamic monitoring of extraction efficiency is achieved through an online detection system, which can monitor changes in various indicators during the extraction process in real time, promptly identifying and resolving problems. When the extraction efficiency falls below a preset standard, the system automatically adjusts operating parameters or initiates a fault diagnosis program to ensure the stability and reliability of the extraction process. S6, adjust the nutrient solution supply parameters according to the second water body state to generate the plant growth state. In one embodiment, the nutrient solution supply system is the core component of the hydroponic unit, responsible for providing the plants with the various nutrients required for growth. Based on the composition analysis results of the second water body, the system dynamically adjusts the nutrient solution formula and supply strategy to maximize the utilization of water resources and optimize plant growth.

[0043] Optionally, this step further includes: step S61, adjusting the supply frequency of the nutrient solution supply device according to the nitrogen concentration in the second water body state. In one embodiment, nitrogen is a key nutrient element for plant growth, directly affecting the plant's growth rate and biomass accumulation. The nitrogen in the second water body state mainly originates from fish metabolites and unextracted protein decomposition products. Nitrogen forms include ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, and organic nitrogen, among others, and different forms of nitrogen have varying utilization efficiency and safety for plants. The nutrient solution supply device employs a precision metering pump and a multi-channel dispensing system, enabling precise control of the supply amount and timing of various nutrients. The device includes components such as a storage tank, metering pump, mixer, filter, and delivery pipelines. The storage tank stores concentrated nutrient solution stock solution, the metering pump is responsible for precise metering and delivery, and the mixer ensures uniform mixing of various nutrients. The adjustment of the supply frequency is based on the plant's nitrogen requirement and the nitrogen supply capacity in the second water body state. Generally, leafy vegetables have a higher nitrogen requirement, and the supply frequency can be set to 4 to 6 times per day. Fruit and vegetable plants have significantly different nitrogen requirements at different growth stages, with higher supply frequency during the vegetative growth stage and appropriately lower frequency during the reproductive growth stage. Root vegetables have relatively lower nitrogen requirements, and the supply frequency can be set to 2 to 4 times per day. In one embodiment, the dynamic adjustment of the supply frequency uses a feedback control algorithm to automatically adjust the supply parameters based on changes in plant leaf color, growth rate, and nutrient solution consumption. When plant leaves show nitrogen deficiency symptoms, the system increases the supply frequency and concentration. When plant growth is too vigorous or excessive vegetative growth occurs, the system appropriately reduces the nitrogen supply. Nitrogen concentration is monitored using methods such as ion-selective electrodes and spectrophotometry to accurately detect different forms of nitrogen. The monitoring frequency is determined based on the plant growth stage and environmental conditions, typically every 2 to 4 hours. Monitoring data is transmitted in real time to the central control unit via a wireless transmission system, providing data support for supply frequency adjustment. Step S62: Adjust the nutrient solution composition ratio according to plant growth needs. In one embodiment, the determination of plant growth needs is based on multiple factors such as plant species, growth stage, environmental conditions, and production goals. Different plants have significantly different nutritional requirements, and even the same plant's nutritional needs can change at different growth stages. Adjusting the composition of nutrient solutions requires comprehensive consideration of these factors to develop personalized nutrient supply plans. The basic components of nutrient solutions include three main categories: macroelements, mesoelements, and microelements. Macroelements include nitrogen, phosphorus, and potassium, which are the three main elements and are the foundation of plant growth. Mesoelements include calcium, magnesium, sulfur, and other elements, which play an important role in plant structure formation and physiological functions. Microelements include iron, manganese, zinc, copper, boron, molybdenum, and other elements, which, although required in smaller amounts, are indispensable for normal plant growth.In one possible implementation, the nutrient solution formulation employs a modular design, preparing different nutrient elements separately as concentrated solutions, which can then be combined and diluted as needed. This design enhances the flexibility of the formulation, facilitating adjustments based on plant requirements and water conditions. The macronutrient module includes three basic formulations: high-nitrogen, balanced, and high-potassium, suitable for different growth stages. Adjustments to component ratios consider the existing nutrient content in the secondary water condition, avoiding redundant additions and nutrient imbalances. For example, if the nitrogen content in the secondary water condition is high, the amount of nitrogen fertilizer added to the nutrient solution is reduced accordingly, while the proportions of other elements such as phosphorus and potassium are increased. If the calcium and magnesium content in the water is insufficient, corresponding micronutrients need to be supplemented in the nutrient solution. The adjustment process also considers the interactions and antagonistic effects between nutrient elements. For example, calcium and magnesium have an antagonistic effect; excessive calcium can affect magnesium absorption. Similar interactions exist between iron and manganese, and zinc and copper. Nutrient solution formulation needs to balance these interactions to ensure that plants can effectively absorb and utilize various nutrients. Monitoring plant growth requirements utilizes plant physiological indicator detection and nutrient diagnostic techniques. Physiological indicators include parameters such as chlorophyll content, photosynthetic efficiency, and root vigor, reflecting the plant's nutritional status and growth vitality. Nutritional diagnosis determines the plant's nutritional status and deficiency symptoms by analyzing the content of various nutrients in plant tissues. Step S63 generates an optimized plant growth state through a multi-layered three-dimensional structure and dynamic light regulation. In one embodiment, the multi-layered three-dimensional structure is an important feature of modern hydroponic systems, maximizing yield per unit area through full utilization of vertical space. The design of the three-dimensional structure needs to consider multiple factors such as plant growth characteristics, light distribution, ventilation conditions, and ease of management. Lightweight, corrosion-resistant, and non-toxic materials are selected for the structure, such as aluminum alloy, stainless steel, or food-grade plastic. The number of layers and spacing of the three-dimensional structure are determined according to the plant species and growth height. For leafy vegetables, the layer spacing is usually set at 30 to 40 cm, and 4 to 6 layers can be set. For fruiting vegetables, the layer spacing needs to be increased to 50 to 80 cm, and the number of layers is reduced accordingly to 2 to 4 layers. The planting density of each layer also needs to be adjusted according to the plant's canopy width and root development. Dynamic light regulation systems are a key technology for successful vertical cultivation, providing suitable light conditions for plants at different levels. The system uses LED light sources, offering advantages such as adjustable spectrum, low energy consumption, and minimal heat generation. The spectral composition of the light source can be adjusted according to the plant's photosynthetic needs, primarily including red, blue, and white light components. In one embodiment, dynamic light regulation employs a layered independent control mode, with each layer equipped with an independent light system and controller. The controller automatically adjusts the light intensity and spectral composition based on the plant's growth stage, photosynthetic needs, and ambient light conditions. During the vegetative growth stage, blue and white light are primarily provided to promote leaf development and stem elongation. During the reproductive growth stage, the proportion of red light is increased to promote flowering and fruiting.Light intensity is typically adjusted between 100 and 500 micromoles per second per square meter, precisely tailored to the plant species and growth requirements. Light duration is programmable, simulating the natural diurnal rhythm of light. Generally, light duration is set to 12 to 16 hours, with a dark period of 8 to 12 hours. In special cases, the photoperiod can be adjusted to induce specific physiological responses in the plants. A ventilation system is an essential component of the three-dimensional structure, responsible for providing fresh air and regulating temperature and humidity. The system includes components such as air inlets, exhaust vents, circulating fans, and filters. Air inlets are located at the bottom of the structure, and exhaust vents at the top, creating a bottom-up airflow circulation. Circulating fans ensure airflow between layers, preventing localized air stagnation. Optimized plant growth is evaluated using multiple indicators, including growth rate, biomass accumulation, nutritional quality, and appearance quality. Growth rate is assessed by measuring plant height, leaf count, and root development. Biomass accumulation is determined through weighing and volume measurements. Nutritional quality includes indicators such as vitamin content, mineral content, and antioxidant content. Appearance quality includes sensory indicators such as color, shape, texture, and freshness. Step S64 involves monitoring plant growth status to determine the effectiveness of nutrient solution adjustments. In one embodiment, plant growth status monitoring is a crucial aspect of hydroponic system management. By continuously tracking plant growth changes, the effectiveness of nutrient solution ratios and management measures is evaluated. The monitoring system employs various technologies, including image recognition, sensor detection, manual observation, and laboratory analysis. Image recognition technology uses high-resolution cameras and image processing algorithms to automatically identify and measure plant morphological characteristics and growth parameters. Cameras are installed above each cultivation rack to periodically capture images of plant growth. Image processing algorithms can automatically identify features such as plant outlines, leaf quantity, leaf area, and color changes. By comparing images at different time points, the system can calculate the plant's growth rate and developmental progress. The sensor detection system includes two main categories: environmental sensors and plant physiological sensors. Environmental sensors monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration, which directly affect plant growth status. Plant physiological sensors include chlorophyll fluorescence sensors, stem flow sensors, and root vitality sensors, which can directly measure the physiological activity status of plants. In one possible implementation, a chlorophyll fluorescence sensor assesses the photosynthetic efficiency and stress status of plants by measuring leaf fluorescence parameters. Under normal conditions, the ratio of maximum fluorescence yield to variable fluorescence in healthy plants should remain above 0.8. This ratio decreases significantly when plants are under nutrient or environmental stress. A stem flow sensor reflects transpiration and water absorption by measuring the flow rate of water in the plant stem. The evaluation of the effect of nutrient solution ratio adjustment uses a comparative analysis method, statistically comparing plant growth data before and after adjustment.Evaluation indicators include the magnitude of growth rate improvement, the degree of improvement in nutritional quality, the proportion of yield increase, and resource utilization efficiency. The magnitude of growth rate improvement is calculated by comparing the daily growth of plants before and after adjustment. The degree of improvement in nutritional quality is determined by analyzing the changes in the content of various nutrients in plant tissues. Laboratory analysis is an important supplement to the evaluation of nutrient solution ratio effects, using precise chemical analysis methods to detect the content of various nutrients in plant tissues. Analytical items include macroelements such as nitrogen, phosphorus, and potassium; mesoelements such as calcium, magnesium, and sulfur; and microelements such as iron, manganese, and zinc. The analytical results are compared with standard values ​​to determine whether the nutritional status of the plants has reached the optimal level. The evaluation of the adjustment effect also considers both economic and environmental benefits. Economic benefits include increased revenue from yield improvement, changes in nutrient solution costs, and changes in energy consumption. Environmental benefits include indicators such as water resource utilization efficiency, nutrient element utilization rate, and reduction in wastewater discharge. The comprehensive evaluation results are used to guide subsequent nutrient solution ratio optimization and system improvement. S7, the treated third water body state is returned to the aquaculture unit, and the aquaculture parameters are adjusted to achieve a synergistic result of fish growth and water quality optimization. In one embodiment, the third water state refers to the water condition after treatment by the hydroponic unit, in which most of the nutrients have been absorbed and utilized by the plants, and the biological filtration of the plant roots further improves the water quality. The water quality characteristics of the third water state are moderate nutrient content, reduced content of harmful substances, sufficient dissolved oxygen, and stable pH value.

[0044] Optionally, this step further includes: step S71, returning the third water body state to the aquaculture unit through standardized pipelines. In one embodiment, the standardized pipeline system is an important infrastructure for realizing water circulation. The pipeline design needs to consider multiple factors such as water flow resistance, cleaning and maintenance, material safety, and system reliability. The pipeline material is food-grade stainless steel or high-density polyethylene, which has good corrosion resistance and biocompatibility. The inner wall of the pipeline is smooth to reduce water flow resistance and bacterial adhesion. The pipeline system adopts a combination of gravity flow and pumping, making full use of the height difference to achieve natural flow and reduce energy consumption. Circulation pumps are installed at the locations requiring lifting, and the pump selection considers parameters such as flow rate requirements, head requirements, and energy efficiency ratio. The pipeline system is also equipped with monitoring instruments such as flow meters, pressure gauges, and thermometers to monitor the water flow status in real time. In one embodiment, the pipeline system adopts a ring layout to form a closed circulation loop. The main pipeline is responsible for the large flow rate of water, and the branch pipelines connect various functional units. Pipeline connections use quick couplings and flange connections for easy disassembly and maintenance. Filters and check valves are installed at key locations to prevent impurities from entering and water from flowing back. The recirculation process also includes water quality regulation, making necessary adjustments to the state of the third water body according to aquaculture needs. Regulation includes temperature control, pH adjustment, dissolved oxygen adjustment, and nutrient supplementation. Temperature regulation is achieved through a heat exchanger, adjusting the water temperature to a range suitable for fish growth. pH adjustment is achieved by adding buffers or acid-base regulators to maintain the acid-base balance of the water. Dissolved oxygen regulation is a crucial part of the recirculation process, introducing oxygen into the water body through an aeration device to increase the dissolved oxygen concentration. The aeration device includes components such as a blower, aeration heads, and air supply pipes. The aeration heads employ a microporous design to generate fine bubbles, improving oxygen dissolution efficiency. The aeration intensity is adjusted according to the dissolved oxygen requirements of the water body and the fish density. Step S72: Based on the water quality data of the third water body state, adjust the feeding amount and dissolved oxygen parameters. In one embodiment, adjusting the feeding amount is a core aspect of aquaculture management, directly affecting fish growth rate, feed conversion rate, and water quality. The adjustment is based on multiple factors, including the nutrient composition of the third water body state, the fish's growth stage, environmental conditions, and production goals. Overfeeding leads to feed waste and water quality deterioration, while underfeeding negatively impacts fish growth. Feeding amounts are calculated based on parameters such as fish weight, growth rate, and feed conversion ratio. Generally, the feeding amount for juvenile fish is 3% to 5% of their body weight, and for adult fish, it is 1% to 3%. Feeding frequency is determined based on the fish's digestive capacity and growth needs, typically 2 to 4 times daily. Feeding times are chosen during periods of high fish activity, such as morning and evening. In one possible implementation, the feeding system employs automated control, using timers and metering devices for precise feeding. The system includes components such as a feed storage bin, conveying device, metering device, and feeder. The feed storage bin is designed to be moisture-proof, insect-proof, and preserve freshness, ensuring feed quality.The conveying system uses either screw conveyors or pneumatic conveyors to transport feed from the storage bins to the feeding points. Dynamic adjustments to the feeding amount are based on observations of fish feeding behavior and monitoring of water quality changes. A video monitoring system is used to observe fish feeding activity and the amount of uneaten feed to determine if the feeding amount is appropriate. When fish are not feeding actively or there is a large amount of uneaten feed, the feeding amount should be reduced appropriately. When fish are feeding actively and there is no uneaten feed, the feeding amount can be increased appropriately. Dissolved oxygen parameters are adjusted considering factors such as fish respiratory needs, water temperature changes, and organic matter decomposition. Different fish species have different dissolved oxygen requirements; cold-water fish generally require higher dissolved oxygen concentrations, while warm-water fish have relatively lower requirements. Increased water temperature reduces oxygen solubility, necessitating increased aeration intensity. Organic matter decomposition consumes dissolved oxygen in the water, requiring adjustments to aeration parameters based on organic matter content. Dissolved oxygen monitoring uses an online dissolved oxygen meter to monitor the dissolved oxygen concentration in the water in real time. Monitoring points are set at key locations in the aquaculture pond, such as the inlet, middle, and outlet, to comprehensively understand the dissolved oxygen distribution. When the dissolved oxygen concentration is lower than the set value, the system automatically starts the aeration device to increase the oxygen supply. When the dissolved oxygen concentration is too high, the system reduces the aeration intensity to avoid energy waste. Optimization of the aeration system also considers factors such as bubble size, distribution uniformity, and energy efficiency. Microbubbles have a larger specific surface area and higher oxygen mass transfer efficiency. The uniformity of bubble distribution affects the dissolved oxygen uniformity of the entire water body, requiring reasonable arrangement of aeration points. Energy efficiency is achieved by selecting high-efficiency blowers and optimizing aeration parameters. Step S73 involves monitoring fish growth status and water quality data to determine the synergistic results of fish growth and water quality optimization. Monitoring fish growth status is an important means of evaluating aquaculture effectiveness and system performance. Monitoring content includes multiple aspects such as fish weight gain, body length changes, health status, and behavioral performance. Weight gain is measured through regular weighing, calculating indicators such as daily weight gain and feed conversion ratio. Body length changes are assessed by measuring fish body length to evaluate fish development. Health status monitoring includes methods such as visual inspection, swimming behavior observation, and physiological indicator testing. Visual inspection mainly observes whether there are wounds, parasites, or lesions on the fish's body surface. Swimming behavior observation includes swimming speed, swimming posture, and group behavior. Physiological indicator detection includes blood indicators, immune indicators, and stress indicators. In one embodiment, fish behavior monitoring uses video analysis technology, continuously recording fish activity through cameras installed above the rearing pond. Video analysis software can automatically identify characteristics such as fish swimming trajectories, activity intensity, and aggregation behavior. Under normal circumstances, healthy fish swim actively, are evenly distributed, and feed readily. When fish exhibit abnormal behavior, such as swimming slowly, gathering in corners, or refusing to eat, it may indicate health problems or environmental stress. Water quality data monitoring covers physical, chemical, and biological indicators. Physical indicators include parameters such as water temperature, transparency, and suspended solids content. Chemical indicators include parameters such as dissolved oxygen, pH value, ammonia nitrogen, nitrite, nitrate, and total phosphorus.Biological indicators include total bacterial count, Escherichia coli, and beneficial bacteria. Monitoring frequency is determined based on the importance and rate of change of each indicator. Key indicators such as dissolved oxygen and pH require continuous monitoring; general indicators such as ammonia nitrogen and nitrite are monitored 1-2 times daily; and special indicators such as heavy metals and pesticide residues are monitored weekly or monthly. Monitoring data is automatically recorded and transmitted through a data acquisition system, forming a complete water quality change archive. The evaluation of synergistic results employs a comprehensive evaluation method, weighting and combining fish growth indicators and water quality indicators. Evaluation indicators include fish growth rate, feed conversion ratio, survival rate, water quality stability, and nutrient utilization rate. When all indicators reach their expected targets, it indicates that the system has achieved a good synergistic effect. The synergistic mechanism of fish growth and water quality optimization is reflected in several aspects. First, stress-induced extraction of bioactive peptides yields valuable products and provides a nutrient source for hydroponics. Second, the purification effect of hydroponic plants on the water improves the aquaculture environment and enhances fish growth conditions. Third, recycling reduces water resource consumption and wastewater discharge, achieving an environmentally friendly production method. The system's collaborative optimization is also reflected in resource allocation and energy utilization. A unified control system coordinates the operation of each unit, avoiding resource conflicts and energy waste. Multi-objective optimization algorithms ensure a balance between various production objectives, maximizing overall efficiency. A dynamic adjustment mechanism enables the system to adapt to changes in the environment and production demands, maintaining long-term stable operation. The accumulation of long-term operational data provides valuable experience for system optimization. By analyzing the production effects under different operating modes, control strategies and parameter settings can be continuously improved. The application of machine learning algorithms gives the system the ability to learn and improve itself; as operating time increases, the system's performance will continuously improve.

[0045] like Figure 2 As shown, in a second aspect, the present invention provides an integrated intelligent aquaponics peptide system for peptide enrichment and hydroponic irrigation, which performs integrated intelligent aquaponics peptide enrichment and hydroponic irrigation using the method described above. The system includes: Input module A1 is used to obtain the preset production strategy selected by the user through the input device to obtain the user-selected operation mode; generation module A2 is used to generate operation instructions for the aquaculture unit, hydroponic unit and peptide extraction unit according to the operation mode using a multi-objective collaborative optimization algorithm; sending module A3 is used to send the operation instructions to the aquaculture unit, hydroponic unit and peptide extraction unit through a standardized interface to obtain the operation status of each unit; adjustment module A4 is used to adjust the water flow parameters of the aquaculture unit according to the operation status to generate a first water state; extraction module A5 is used to extract bioactive peptides from the first water state to generate a second water state; supply module A6 is used to adjust the nutrient solution supply parameters according to the second water state to generate a plant growth state; coordination module A7 is used to return the treated third water state to the aquaculture unit to adjust the aquaculture parameters and achieve a synergistic result of fish growth and water quality optimization.

[0046] This system achieves intelligent control throughout the entire process from aquaculture to plant cultivation through the collaborative work of its various modules. In practical applications, it significantly improves resource utilization efficiency, reduces the need for manual intervention, and ensures a healthy growth environment for both fish and plants. During system operation, real-time data exchange between units ensures the dynamic adjustment capability of production strategies. Furthermore, by applying multi-objective optimization algorithms, the system can automatically match optimal parameters at different production stages, thereby meeting diverse production needs. This integrated design not only simplifies the operation process but also enhances the overall system's stability and sustainability.

[0047] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for integrating peptide solution enrichment and water culture irrigation with intelligent fish peptide, characterized in that, The method comprises the following steps: S1, obtaining a preset production strategy selected by a user through an input device to obtain a running mode selected by the user; S2, generating running instructions of a breeding unit, a hydroponic unit and a peptide extraction unit according to the running mode by using a multi-objective collaborative optimization algorithm; S3, sending the running instructions to the breeding unit, the hydroponic unit and the peptide extraction unit through a standardized interface to obtain a running state of each unit; S4, adjusting a water flow parameter of the breeding unit according to the running state to generate a first water body state; S5, extracting bioactive peptides from the first water body state to generate a second water body state; S6, adjusting a nutrient solution supply parameter according to the second water body state to generate a plant growth state; S7, returning the processed third water body state to the breeding unit to adjust a breeding parameter, so as to realize a collaborative result of fish growth and water quality optimization.

2. The method of claim 1, wherein, The step S1 further comprises the following steps: S11, obtaining a preset production strategy input by a user through a touch screen, wherein the preset production strategy comprises a peptide priority mode, a fish weight gain priority mode and a high yield of vegetables mode; S12, determining a running mode according to the user input to generate a priority weight corresponding to the running mode; S13, transmitting the priority weight to a central control unit to generate running instructions of each unit.

3. The method of claim 2, wherein, The step S12 further comprises that in the peptide priority mode, a weight of bioactive peptide extraction is set to 60% to 70% of a total target weight, a weight of fish growth is set to 20% to 25% of the total target weight, and a weight of plant cultivation is set to 10% to 15% of the total target weight.

4. The method of claim 2, wherein, The step S12 further comprises that in the fish weight gain priority mode, the weight of fish growth is set to 50% to 60% of the total target weight, the weight of bioactive peptide extraction is set to 25% to 30% of the total target weight, and the weight of plant cultivation is set to 15% to 20% of the total target weight.

5. The method of claim 2, wherein, The step S12 further comprises that in the high yield of vegetables mode, the weight of plant cultivation is set to 45% to 55% of the total target weight, the weight of fish growth is set to 30% to 35% of the total target weight, and the weight of bioactive peptide extraction is set to 15% to 20% of the total target weight.

6. The method of claim 1, wherein, The step S2 further comprises the following steps: S21, determining running parameter weights of the breeding unit, the hydroponic unit and the peptide extraction unit according to the running mode; S22, dynamically adjusting the running parameter weights by using the multi-objective collaborative optimization algorithm to generate collaborative running instructions of each unit; S23, determining a running frequency and a parameter range of each unit according to the collaborative running instructions.

7. The method of claim 1, wherein, The step S3 further comprises the following steps: S31, transmitting the running instructions to the breeding unit, the hydroponic unit and the peptide extraction unit through a standardized circuit connector; S32, obtaining running feedback data of each unit to determine a collaborative running state of each unit; S33, updating control parameters of the central control unit according to the collaborative running state.

8. The method of claim 1, wherein, The step S4 further comprises the following steps: S41, if the running mode is the peptide priority mode, performing a high-frequency stress induction program; Step S42, adjusting the water flow speed by an intermittent water flow speed adjustment program to induce stress response of fish; Step S43, monitoring the first water body state according to water quality data to determine the stress inducing factor in the water body.

9. The method of claim 8, wherein, The step S42 further comprises: the water flow speed adjustment program adopts a three-stage adjustment mode, the first stage is a slow acceleration stage, the water flow speed gradually increases from the static state to the target speed, and the acceleration time is 3-5 minutes; the second stage is a constant speed maintenance stage, the water flow keeps the target speed for 10-15 minutes; the third stage is a slow deceleration stage, the water flow speed gradually decreases to the static state, and the deceleration time is 2-3 minutes.

10. A peptide solution enrichment and water culture irrigation intelligent fish peptide integrated system, characterized in that, The method is used for performing peptide solution enrichment and water culture irrigation intelligent fish peptide integration, and the system comprises: An input module (A1) is configured to obtain a preset production strategy selected by a user through an input device, and obtain a running mode selected by the user. A generation module (A2) is configured to generate running instructions of a breeding unit, a water culture unit and a peptide extraction unit according to the running mode by using a multi-target collaborative optimization algorithm. A sending module (A3) is configured to send the running instructions to the breeding unit, the water culture unit and the peptide extraction unit through a standardized interface, and obtain running states of the units. An adjustment module (A4) is configured to adjust water flow parameters of the breeding unit according to the running states, and generate a first water body state. An extraction module (A5) is configured to extract bioactive peptides from the first water body state, and generate a second water body state. A supply module (A6) is configured to adjust nutrient solution supply parameters according to the second water body state, and generate a plant growth state. A collaborative module (A7) is configured to return the processed third water body state to the breeding unit, adjust breeding parameters, and achieve a collaborative result of fish growth and water quality optimization.

Images